Prediction of academic achievement in adolescents from teacher reports of inattention in childhood - a pattern classification study

AJ Lundervold, T Bø, A Lundervold

~GitHub/inattention-populationsample/code/inattention-data-prep.Rmd

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Organization of the data and the analysis:

Data preparation

Input file:

• inattention_Arvid_new.sav (from Astri, on ~/Dropbox/Arvid_inattention/data2)
• inattention_Astri_94_96_new_grades_updated.sav (on ~/Dropbox/Arvid_inattention/data2) [Correct age]
• Alternatively: D <- read.csv(file = “../data/inattention_nomiss_2397x12.csv”)

Output files (data):

• inattention_nomiss_2397x12.csv
• inattention_nomiss_2397x12_snap_is_0_1_2.csv
• inattention_nomiss_2397x12_snap_is_0_1.csv
• inattention_nomiss_2397x12_snap_is_0_1_2_outcome_is_L_M_H.csv (Low, Medium, High academic score)
• inattention_nomiss_2397x12_snap_is_0_1_2_outcome_is_0_1_2.csv (all numerical)
• inattention_nomiss_2397x12_snap_is_N_S_C_outcome_is_L_M_H.csv (Not, Somewhat, Certainly true)

 # D <- read.csv(file = "../data/inattention_nomiss_2397x12.csv")

# The original SPSS file as provided to AJL is
# 'inattention_Astri_94_96_new_grades_updated.sav'
# and being edited and reduced by AJL to 'inattention_Arvid_new.sav'
# Import data stored in the SPSS format
library(memisc)

Loading required package: lattice

Attaching package: ‘lattice’

The following object is masked from ‘package:boot’:

    melanoma

Loading required package: MASS

Attaching package: ‘memisc’

The following object is masked from ‘package:BBmisc’:

    %nin%

The following objects are masked from ‘package:stats’:

    contr.sum, contr.treatment, contrasts

The following object is masked from ‘package:base’:

    as.array

# fn <- "../data/inattention_Arvid_new.sav"
fn <- "/Users/arvid/Dropbox/Arvid_inattention/data2/inattention_Arvid_new.sav"
data <- as.data.set(spss.system.file(fn))
library(foreign)
fn_age <- "/Users/arvid/Dropbox/Arvid_inattention/data2/inattention_Astri_94_96_new_grades_updated.sav"
Sys.getlocale()

[1] "en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8"

#Sys.setlocale(locale="C")
data_age <- read.spss(fn_age, to.data.frame=TRUE, use.value.labels=FALSE)

/Users/arvid/Dropbox/Arvid_inattention/data2/inattention_Astri_94_96_new_grades_updated.sav: Unrecognized record type 7, subtype 14 encountered in system file/Users/arvid/Dropbox/Arvid_inattention/data2/inattention_Astri_94_96_new_grades_updated.sav: Unrecognized record type 7, subtype 18 encountered in system file/Users/arvid/Dropbox/Arvid_inattention/data2/inattention_Astri_94_96_new_grades_updated.sav: Unrecognized record type 7, subtype 24 encountered in system filere-encoding from latin1

#names(data_age)
dim(data_age)

[1] 10870   496

age_c4 = data_age$c_4_age_at_completion 
summary(age_c4)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  16.02   16.73   17.40   17.49   18.17   19.99     364 

# Make new data frame from the sample with the variables 
# gender, grade, SNAP1, ..., SNAP9 (vars #1-11) and
# academic_achievement (var #52) 
names(data)

 [1] "gender"               "grade"                "snap1"                "snap2"               
 [5] "snap3"                "snap4"                "snap5"                "snap6"               
 [9] "snap7"                "snap8"                "snap9"                "snap10"              
[13] "snap11"               "snap12"               "snap13"               "snap14"              
[17] "snap15"               "snap16"               "snap17"               "snap18"              
[21] "y_4_asrs_1"           "y_4_asrs_2"           "y_4_asrs_3"           "y_4_asrs_4"          
[25] "y_4_asrs_5"           "y_4_asrs_6"           "y_4_asrs_7"           "y_4_asrs_8"          
[29] "y_4_asrs_9"           "y_4_asrs_10"          "y_4_asrs_11"          "y_4_asrs_12"         
[33] "y_4_asrs_13"          "y_4_asrs_14"          "y_4_asrs_15"          "y_4_asrs_16"         
[37] "y_4_asrs_17"          "y_4_asrs_18"          "y_4_mfq_1"            "y_4_mfq_2"           
[41] "y_4_mfq_3"            "y_4_mfq_4"            "y_4_mfq_5"            "y_4_mfq_6"           
[45] "y_4_mfq_7"            "y_4_mfq_8"            "y_4_mfq_9"            "y_4_mfq_10"          
[49] "y_4_mfq_11"           "y_4_mfq_12"           "y_4_mfq_13"           "academic_achievement"

d <- data[, c(1:11, 52)]
dim(d)

[1] 10870    12

names(d)

 [1] "gender"               "grade"                "snap1"                "snap2"               
 [5] "snap3"                "snap4"                "snap5"                "snap6"               
 [9] "snap7"                "snap8"                "snap9"                "academic_achievement"

str(d)

Data set with 10870 obs. of 12 variables:
 $ gender              : Nmnl. item w/ 2 labels for 0,1  num  NA NA NA NA NA NA NA NA NA NA ...
 $ grade               : Itvl. item + ms.v.  num  NA NA NA NA NA NA NA NA NA NA ...
 $ snap1               : Nmnl. item w/ 3 labels for 0,1,2 + ms.v.  num  NA NA NA NA NA NA NA NA NA NA ...
 $ snap2               : Nmnl. item w/ 3 labels for 0,1,2 + ms.v.  num  NA NA NA NA NA NA NA NA NA NA ...
 $ snap3               : Nmnl. item w/ 3 labels for 0,1,2 + ms.v.  num  NA NA NA NA NA NA NA NA NA NA ...
 $ snap4               : Nmnl. item w/ 3 labels for 0,1,2 + ms.v.  num  NA NA NA NA NA NA NA NA NA NA ...
 $ snap5               : Nmnl. item w/ 3 labels for 0,1,2 + ms.v.  num  NA NA NA NA NA NA NA NA NA NA ...
 $ snap6               : Nmnl. item w/ 3 labels for 0,1,2 + ms.v.  num  NA NA NA NA NA NA NA NA NA NA ...
 $ snap7               : Nmnl. item w/ 3 labels for 0,1,2 + ms.v.  num  NA NA NA NA NA NA NA NA NA NA ...
 $ snap8               : Nmnl. item w/ 3 labels for 0,1,2 + ms.v.  num  NA NA NA NA NA NA NA NA NA NA ...
 $ snap9               : Nmnl. item w/ 3 labels for 0,1,2 + ms.v.  num  NA NA NA NA NA NA NA NA NA NA ...
 $ academic_achievement: Itvl. item  num  2.86 NA 3 3.67 4.1 ...

summary(d)

  gender          grade                    snap1                 snap2                 snap3     
 Girl:5528   Min.    :   2.00   Not true      :2646   Not true      :2698   Not true      :2810  
 Boy :4978   1st Qu. :   2.00   Somewhat true : 350   Somewhat true : 294   Somewhat true : 225  
 *   :   0   Median  :   3.00   Certainly true:  61   Certainly true:  65   Certainly true:  23  
 NAs : 364   Mean    :   2.84   *             :   0   *             :   0   *             :   0  
             3rd Qu. :   3.50   NAs           :7813   NAs           :7813   NAs           :7812  
             Max.    :   4.00                                                                    
             Missings:   0.00                                                                    
             NAs     :7719.00                                                                    
            snap4                 snap5                 snap6                 snap7     
 Not true      :2806   Not true      :2783   Not true      :2784   Not true      :2927  
 Somewhat true : 229   Somewhat true : 225   Somewhat true : 223   Somewhat true :  96  
 Certainly true:  22   Certainly true:  49   Certainly true:  49   Certainly true:  18  
 *             :   0   *             :   0   *             :   0   *             :   0  
 NAs           :7813   NAs           :7813   NAs           :7814   NAs           :7829  
                                                                                        
                                                                                        
                                                                                        
            snap8                 snap9      academic_achievement
 Not true      :2260   Not true      :2733   Min.    :   1.000   
 Somewhat true : 669   Somewhat true : 288   1st Qu. :   3.286   
 Certainly true: 127   Certainly true:  37   Median  :   3.889   
 *             :   0   *             :   0   Mean    :   3.824   
 NAs           :7814   NAs           :7812   3rd Qu. :   4.444   
                                             Max.    :   6.000   
                                             Missings:   0.000   
                                             NAs     :2204.000   

dd <- d
dd$age <- age_c4
summary(dd)

  gender          grade                    snap1                 snap2                 snap3     
 Girl:5528   Min.    :   2.00   Not true      :2646   Not true      :2698   Not true      :2810  
 Boy :4978   1st Qu. :   2.00   Somewhat true : 350   Somewhat true : 294   Somewhat true : 225  
 *   :   0   Median  :   3.00   Certainly true:  61   Certainly true:  65   Certainly true:  23  
 NAs : 364   Mean    :   2.84   *             :   0   *             :   0   *             :   0  
             3rd Qu. :   3.50   NAs           :7813   NAs           :7813   NAs           :7812  
             Max.    :   4.00                                                                    
             Missings:   0.00                                                                    
             NAs     :7719.00                                                                    
            snap4                 snap5                 snap6                 snap7     
 Not true      :2806   Not true      :2783   Not true      :2784   Not true      :2927  
 Somewhat true : 229   Somewhat true : 225   Somewhat true : 223   Somewhat true :  96  
 Certainly true:  22   Certainly true:  49   Certainly true:  49   Certainly true:  18  
 *             :   0   *             :   0   *             :   0   *             :   0  
 NAs           :7813   NAs           :7813   NAs           :7814   NAs           :7829  
                                                                                        
                                                                                        
                                                                                        
            snap8                 snap9      academic_achievement      age       
 Not true      :2260   Not true      :2733   Min.    :   1.000    Min.   :16.02  
 Somewhat true : 669   Somewhat true : 288   1st Qu. :   3.286    1st Qu.:16.73  
 Certainly true: 127   Certainly true:  37   Median  :   3.889    Median :17.40  
 *             :   0   *             :   0   Mean    :   3.824    Mean   :17.49  
 NAs           :7814   NAs           :7812   3rd Qu. :   4.444    3rd Qu.:18.17  
                                             Max.    :   6.000    Max.   :19.99  
                                             Missings:   0.000    NA's   :364    
                                             NAs     :2204.000                   

# Get observations of data frame that have missing values and those with complete cases
library(psych)
d.miss <- d[!complete.cases(d),]
d.nomiss <- d[complete.cases(d),]
str(d.nomiss)

Data set with 2397 obs. of 12 variables:
 $ gender              : Nmnl. item w/ 2 labels for 0,1  num  0 0 0 0 0 0 0 0 0 0 ...
 $ grade               : Itvl. item + ms.v.  num  2 2 2 2 2 2 2 2 2 2 ...
 $ snap1               : Nmnl. item w/ 3 labels for 0,1,2 + ms.v.  num  0 0 0 0 0 0 0 0 0 0 ...
 $ snap2               : Nmnl. item w/ 3 labels for 0,1,2 + ms.v.  num  0 0 0 0 0 0 0 0 0 0 ...
 $ snap3               : Nmnl. item w/ 3 labels for 0,1,2 + ms.v.  num  0 1 0 0 0 0 0 0 0 0 ...
 $ snap4               : Nmnl. item w/ 3 labels for 0,1,2 + ms.v.  num  0 0 0 0 0 0 0 0 0 0 ...
 $ snap5               : Nmnl. item w/ 3 labels for 0,1,2 + ms.v.  num  0 0 0 0 0 0 0 0 0 0 ...
 $ snap6               : Nmnl. item w/ 3 labels for 0,1,2 + ms.v.  num  0 0 0 0 0 0 0 0 0 0 ...
 $ snap7               : Nmnl. item w/ 3 labels for 0,1,2 + ms.v.  num  0 0 0 0 0 0 0 0 0 0 ...
 $ snap8               : Nmnl. item w/ 3 labels for 0,1,2 + ms.v.  num  0 0 0 0 0 0 0 0 1 0 ...
 $ snap9               : Nmnl. item w/ 3 labels for 0,1,2 + ms.v.  num  0 0 0 0 0 0 0 0 0 0 ...
 $ academic_achievement: Itvl. item  num  4.67 3.67 4.14 4.11 4.3 ...

headTail(as.data.frame(d.nomiss))

summary(d.nomiss)

  gender         grade                  snap1                 snap2                 snap3     
 Girl:1256   Min.   :2.000   Not true      :2079   Not true      :2117   Not true      :2201  
 Boy :1141   1st Qu.:2.000   Somewhat true : 272   Somewhat true : 230   Somewhat true : 181  
             Median :3.000   Certainly true:  46   Certainly true:  50   Certainly true:  15  
             Mean   :2.814                                                                    
             3rd Qu.:3.000                                                                    
             Max.   :4.000                                                                    
            snap4                 snap5                 snap6                 snap7     
 Not true      :2217   Not true      :2190   Not true      :2195   Not true      :2312  
 Somewhat true : 164   Somewhat true : 176   Somewhat true : 170   Somewhat true :  73  
 Certainly true:  16   Certainly true:  31   Certainly true:  32   Certainly true:  12  
                                                                                        
                                                                                        
                                                                                        
            snap8                 snap9      academic_achievement
 Not true      :1794   Not true      :2142   Min.   :1.000       
 Somewhat true : 510   Somewhat true : 228   1st Qu.:3.556       
 Certainly true:  93   Certainly true:  27   Median :4.083       
                                             Mean   :4.023       
                                             3rd Qu.:4.556       
                                             Max.   :5.900       

D1 <- d.nomiss   # For later use
dd.nomiss <- dd[complete.cases(dd),]
summary(dd.nomiss)

  gender         grade                  snap1                 snap2                 snap3     
 Girl:1256   Min.   :2.000   Not true      :2079   Not true      :2117   Not true      :2201  
 Boy :1141   1st Qu.:2.000   Somewhat true : 272   Somewhat true : 230   Somewhat true : 181  
             Median :3.000   Certainly true:  46   Certainly true:  50   Certainly true:  15  
             Mean   :2.814                                                                    
             3rd Qu.:3.000                                                                    
             Max.   :4.000                                                                    
            snap4                 snap5                 snap6                 snap7     
 Not true      :2217   Not true      :2190   Not true      :2195   Not true      :2312  
 Somewhat true : 164   Somewhat true : 176   Somewhat true : 170   Somewhat true :  73  
 Certainly true:  16   Certainly true:  31   Certainly true:  32   Certainly true:  12  
                                                                                        
                                                                                        
                                                                                        
            snap8                 snap9      academic_achievement      age       
 Not true      :1794   Not true      :2142   Min.   :1.000        Min.   :16.06  
 Somewhat true : 510   Somewhat true : 228   1st Qu.:3.556        1st Qu.:16.69  
 Certainly true:  93   Certainly true:  27   Median :4.083        Median :17.32  
                                             Mean   :4.023        Mean   :17.40  
                                             3rd Qu.:4.556        3rd Qu.:18.03  
                                             Max.   :5.900        Max.   :19.22  

For the manuscript

ss = summary(D1$gender)

Running a t-test regarding age and gender

tt <- with(subset(dd.nomiss, gender %in% c("Girl", "Boy")),
     t.test(age ~ factor(gender)))
tt

    Welch Two Sample t-test

data:  age by factor(gender)
t = 2.1129, df = 2384.7, p-value = 0.03472
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.005156208 0.138277404
sample estimates:
mean in group Girl  mean in group Boy 
          17.43525           17.36354 

.. and information about gender and academic achievement when they participated in the fourth study wave - in total 2397 participants, 1256 Girls and 1141 Boys. Mean age when included in wave 4 was 17.4011148 (16.95) years and SD 0.8317614 (SD = .846), with a slightly higher mean age in girls compared to boys (p=0.0347158)

nonsignificant age-difference between girls and boys (p = .088).

! Make a table of SNAP1-9 distribution according to Not true (N), Somewhat true (S), Certainly true (C) for boys and girls separately

summary(D1$snap1[D1$gender == "Boy"])

      Not true  Somewhat true Certainly true 
           935            176             30 

summary(D1$snap1[D1$gender == "Girl"])

      Not true  Somewhat true Certainly true 
          1144             96             16 

# Association Statistics
# Computes the Pearson chi-Squared test, the Likelihood Ratio chi-Squared test, 
# the phi coefficient, the contingency coefficient and Cramer's V for possibly 
# stratified contingency tables.
library(vcd)

Loading required package: grid

Attaching package: ‘grid’

The following object is masked from ‘package:BBmisc’:

    explode

tab <- xtabs(~gender + grade, data = D1)
summary(assocstats(tab))

Call: xtabs(formula = ~gender + grade, data = D1)
Number of cases in table: 2397 
Number of factors: 2 
Test for independence of all factors:
    Chisq = 8.373, df = 2, p-value = 0.0152
                    X^2 df P(> X^2)
Likelihood Ratio 8.3991  2 0.015002
Pearson          8.3731  2 0.015199

Phi-Coefficient   : NA 
Contingency Coeff.: 0.059 
Cramer's V        : 0.059 

# Save the nomiss D to an .csv file without row names for further analysis
D <- d.nomiss
write.csv(D, file = "../data/inattention_nomiss_2397x12.csv",row.names=FALSE)

# For simplicity, we rename (and translate) the variables names in the dataset D without any missing
library(plyr)

Attaching package: ‘plyr’

The following object is masked from ‘package:memisc’:

    rename

d.nomiss <- read.csv(file = "../data/inattention_nomiss_2397x12.csv")
D <- d.nomiss
D <- rename(D, c(academic_achievement="ave"))
D$ave <- as.numeric(D$ave)
D$snap1 <- mapvalues(as.factor(D$snap1), from = c("Not true","Somewhat true","Certainly true"), to = c("0","1","2"))
D$snap1 <- as.numeric(D$snap1)-1
D$snap2 <- mapvalues(as.factor(D$snap2), from = c("Not true","Somewhat true","Certainly true"), to = c("0","1","2"))
D$snap2 <- as.numeric(D$snap2)-1
D$snap3 <- mapvalues(as.factor(D$snap3), from = c("Not true","Somewhat true","Certainly true"), to = c("0","1","2"))
D$snap3 <- as.numeric(D$snap3)-1
D$snap4 <- mapvalues(as.factor(D$snap4), from = c("Not true","Somewhat true","Certainly true"), to = c("0","1","2"))
D$snap4 <- as.numeric(D$snap4)-1
D$snap5 <- mapvalues(as.factor(D$snap5), from = c("Not true","Somewhat true","Certainly true"), to = c("0","1","2"))
D$snap5 <- as.numeric(D$snap5)-1
D$snap6 <- mapvalues(as.factor(D$snap6), from = c("Not true","Somewhat true","Certainly true"), to = c("0","1","2"))
D$snap6 <- as.numeric(D$snap6)-1
D$snap7 <- mapvalues(as.factor(D$snap7), from = c("Not true","Somewhat true","Certainly true"), to = c("0","1","2"))
D$snap7 <- as.numeric(D$snap7)-1
D$snap8 <- mapvalues(as.factor(D$snap8), from = c("Not true","Somewhat true","Certainly true"), to = c("0","1","2"))
D$snap8 <- as.numeric(D$snap8)-1
D$snap9 <- mapvalues(as.factor(D$snap9), from = c("Not true","Somewhat true","Certainly true"), to = c("0","1","2"))
D$snap9 <- as.numeric(D$snap9)-1
D$gender <- mapvalues(as.factor(D$gender), from = c("Girl", "Boy"), to = c("0", "1"))
D$gender <- as.numeric(D$gender)-1
D$grade <- as.numeric(D$grade)
str(D)

'data.frame':   2397 obs. of  12 variables:
 $ gender: num  1 1 1 1 1 1 1 1 1 1 ...
 $ grade : num  2 2 2 2 2 2 2 2 2 2 ...
 $ snap1 : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap2 : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap3 : num  1 2 1 1 1 1 1 1 1 1 ...
 $ snap4 : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap5 : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap6 : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap7 : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap8 : num  1 1 1 1 1 1 1 1 2 1 ...
 $ snap9 : num  1 1 1 1 1 1 1 1 1 1 ...
 $ ave   : num  4.67 3.67 4.14 4.11 4.3 ...

headTail(D)

D3 <- D   # For later use

# Save D (at early stage) to an .csv file for later analysis in R or MATLAB 
write.csv(D, file = "../data/inattention_nomiss_2397x12_snap_is_0_1_2.csv",row.names=FALSE)

# For even more simplicity, we rename (and translate) the variables names in the dataset 
# without any missing, reducing the predictor categories to be binary, 
# i.e. collapsing SNAP values "1" and "2" to "1":
library(plyr)
D <- d.nomiss
D <- rename(D, c(academic_achievement="ave"))
D$ave <- as.numeric(D$ave)
D$snap1 <- mapvalues(as.factor(D$snap1), from = c("Not true","Somewhat true","Certainly true"), to = c("0","1","1"))
D$snap1 <- as.numeric(D$snap1)-1
D$snap2 <- mapvalues(as.factor(D$snap2), from = c("Not true","Somewhat true","Certainly true"), to = c("0","1","1"))
D$snap2 <- as.numeric(D$snap2)-1
D$snap3 <- mapvalues(as.factor(D$snap3), from = c("Not true","Somewhat true","Certainly true"), to = c("0","1","1"))
D$snap3 <- as.numeric(D$snap3)-1
D$snap4 <- mapvalues(as.factor(D$snap4), from = c("Not true","Somewhat true","Certainly true"), to = c("0","1","1"))
D$snap4 <- as.numeric(D$snap4)-1
D$snap5 <- mapvalues(as.factor(D$snap5), from = c("Not true","Somewhat true","Certainly true"), to = c("0","1","1"))
D$snap5 <- as.numeric(D$snap5)-1
D$snap6 <- mapvalues(as.factor(D$snap6), from = c("Not true","Somewhat true","Certainly true"), to = c("0","1","1"))
D$snap6 <- as.numeric(D$snap6)-1
D$snap7 <- mapvalues(as.factor(D$snap7), from = c("Not true","Somewhat true","Certainly true"), to = c("0","1","1"))
D$snap7 <- as.numeric(D$snap7)-1
D$snap8 <- mapvalues(as.factor(D$snap8), from = c("Not true","Somewhat true","Certainly true"), to = c("0","1","1"))
D$snap8 <- as.numeric(D$snap8)-1
D$snap9 <- mapvalues(as.factor(D$snap9), from = c("Not true","Somewhat true","Certainly true"), to = c("0","1","1"))
D$snap9 <- as.numeric(D$snap9)-1
D$gender <- mapvalues(as.factor(D$gender), from = c("Girl", "Boy"), to = c("0", "1"))
D$gender <- as.numeric(D$gender)-1
D$grade <- as.numeric(D$grade)
str(D)

'data.frame':   2397 obs. of  12 variables:
 $ gender: num  1 1 1 1 1 1 1 1 1 1 ...
 $ grade : num  2 2 2 2 2 2 2 2 2 2 ...
 $ snap1 : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap2 : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap3 : num  1 0 1 1 1 1 1 1 1 1 ...
 $ snap4 : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap5 : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap6 : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap7 : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap8 : num  1 1 1 1 1 1 1 1 0 1 ...
 $ snap9 : num  1 1 1 1 1 1 1 1 1 1 ...
 $ ave   : num  4.67 3.67 4.14 4.11 4.3 ...

headTail(D)

D2 <- D  # For later use

# Save the new D to an .csv file without row names for further analysis
write.csv(D, file = "../data/inattention_nomiss_2397x12_snap_is_0_1.csv",row.names=FALSE)

Struture of the D dataset

D <- D3
s <- dim(D)
n <- s[1]
p <- s[2]
txt = sprintf("Structure of the %d x %d DATASET", n, p)
print(txt)

[1] "Structure of the 2397 x 12 DATASET"

library(DiagrammeR)
n_txt = sprintf("Dataset \n (N = %d)", n);
gviz <- grViz("
              # Circles: predictor variables; Triangle: Outcome variable
              digraph Structure_of_the_dataset_D {
              # node definitions with substituted label text
              node [fontname = Helvetica]
              1 [label = 'Dataset \n (N = 2397)', shape=box]
              2 [label = 'gender \n {Girl (0) | Boy (1)}', shape=circle]
              3 [label = 'grade \n {2 | 3 | 4}', shape=circle]
              4 [label = 'ave \n (average marks) \n [1, 6] or {low (L) | medium (M) | high (H)}', shape=triangle]
              a [label = 'SNAP \n {0 | 1 | 2}', shape=oval]
              b [label = 'SNAP1', shape=circle]
              c [label = 'SNAP2', shape=circle]
              d [label = 'SNAP3', shape=circle]
              e [label = 'SNAP4', shape=circle]
              f [label = 'SNAP5', shape=circle]
              g [label = 'SNAP6', shape=circle]
              h [label = 'SNAP7', shape=circle]
              i [label = 'SNAP8', shape=circle]
              j [label = 'SNAP9', shape=circle]
              # edge definitions with the node IDs
              1 -> {2 3 a 4}
              a -> {b c d e f g h i j}
              }",
engine = "dot")
print(gviz)

NULL

# This does not work using DiagrammeR / GraphViz
# png("../manuscript/Figs/graph_design.png")
# print(gviz)
# dev.off()
# Uses Viewer, Zoom and Screen capture to produce .png and then
# data_prep_structure_grviz_20160203.pdf file

The dataset that will be analyzed and reported

In our analysis we included n = 2397 individuals (none with missing data) from the dataset “/Users/arvid/Dropbox/Arvid_inattention/data2/inattention_Arvid_new.sav”.

D <- D3
n_txt = sprintf("In our analysis we included n = %d individuals (none with missing data) from the dataset '%s'\n", nrow(D), fn);
print(n_txt)

[1] "In our analysis we included n = 2397 individuals (none with missing data) from the dataset '/Users/arvid/Dropbox/Arvid_inattention/data2/inattention_Arvid_new.sav'\n"

Grades (continuous and categorized)

We consider the grades (academic_achievement), as both a continuous (for regression) and discretized variable (for classification), where gjennomsnitt: - Item ‘Karaktergjennomsnitt alle gyldige karakterer 1-6 (ikke kroppsøving)’

# Discretized at three levels, with data-driven cutpoints (equifrequent levels)
D <- D3
aver <- D$ave
summary(aver)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   3.556   4.083   4.023   4.556   5.900 

bins <- 3
cutpoints<-quantile(aver,(0:bins)/bins,names=FALSE)
print(cutpoints)

[1] 1.000000 3.750000 4.428571 5.900000

# Consistent with MATLAB 'histcounts' (D_20151110_analysis.m  ;  T2)
# fn2 = '../data/D_20151110.csv';
# T2 = readtable(fn2);
# bins = 3;
# y = quantile(T2.ave,[0:bins]/bins)
# [N,EDGES,BIN] = histcounts(T2.ave,y);
# cuts = sprintf('1:[%.2f, %.2f) 2:[%.2f,%.2f) 3:[%.2f,%.2f]', EDGES(1), EDGES(2), EDGES(2), EDGES(3), EDGES(3), EDGES(4));
# T2.ave_cat = BIN;   % categorical(BIN,'Ordinal',true);
# descr = sprintf('%s - 1:low, 2:medium; 3:high average mark', cuts);
# T2.Properties.VariableDescriptions{'ave_cat'} = descr;
# => descr = 1:[1.00, 3.75) 2:[3.75,4.43) 3:[4.43,5.90] - 1:low, 2:medium; 3:high average mark

averBinned <- cut(aver, cutpoints, right=FALSE, include.lowest=TRUE)
summary(averBinned)

   [1,3.75) [3.75,4.43)  [4.43,5.9] 
        779         818         800 

Make histogram of dicretized ‘averBinned’:

hist(as.numeric(averBinned))

Define grade categories “low”, “medium” and “high” in terms of the calculated cut-point intervals:

txt_low <- sprintf("low (L): [%.3f, %.3f)\n", cutpoints[[1]], cutpoints[[2]])
print(txt_low)

[1] "low (L): [1.000, 3.750)\n"

txt_medium <- sprintf("medium (M): [%.3f, %.3f)\n", cutpoints[[2]], cutpoints[[3]])
print(txt_medium)

[1] "medium (M): [3.750, 4.429)\n"

txt_high <- sprintf("high H): [%.3f, %.3f]\n", cutpoints[[3]], cutpoints[[4]])
print(txt_high)

[1] "high H): [4.429, 5.900]\n"

library(psych)
# Dataset for classification based on D3 and discretized average academic achievemnt
C <- D3
C$averBinned <- cut(aver, cutpoints, right=FALSE, include.lowest=TRUE,
                     labels=c("L","M","H"))
C <- subset(C, select = -c(ave))
str(C)

'data.frame':   2397 obs. of  12 variables:
 $ gender    : num  1 1 1 1 1 1 1 1 1 1 ...
 $ grade     : num  2 2 2 2 2 2 2 2 2 2 ...
 $ snap1     : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap2     : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap3     : num  1 2 1 1 1 1 1 1 1 1 ...
 $ snap4     : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap5     : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap6     : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap7     : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap8     : num  1 1 1 1 1 1 1 1 2 1 ...
 $ snap9     : num  1 1 1 1 1 1 1 1 1 1 ...
 $ averBinned: Factor w/ 3 levels "L","M","H": 3 1 2 2 2 2 1 2 3 2 ...

headTail(as.data.frame(C))

headTail(as.data.frame(D3))

# Save the dataset C with binary SNAP predictors and trinary outcome to an .csv file 
# for further analysis
write.csv(C, file = "../data/inattention_nomiss_2397x12_snap_is_0_1_2_outcome_is_L_M_H.csv",row.names=FALSE)

# Dataset for classification based on D3 and discretized average academic achievemnt
E <- D3
E$averBinned <- cut(aver, cutpoints, right=FALSE, include.lowest=TRUE,
                     labels=c("0","1","2"))
E <- subset(E, select = -c(ave))
str(E)

'data.frame':   2397 obs. of  12 variables:
 $ gender    : num  1 1 1 1 1 1 1 1 1 1 ...
 $ grade     : num  2 2 2 2 2 2 2 2 2 2 ...
 $ snap1     : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap2     : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap3     : num  1 2 1 1 1 1 1 1 1 1 ...
 $ snap4     : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap5     : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap6     : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap7     : num  1 1 1 1 1 1 1 1 1 1 ...
 $ snap8     : num  1 1 1 1 1 1 1 1 2 1 ...
 $ snap9     : num  1 1 1 1 1 1 1 1 1 1 ...
 $ averBinned: Factor w/ 3 levels "0","1","2": 3 1 2 2 2 2 1 2 3 2 ...

summary(E)

     gender          grade           snap1           snap2           snap3           snap4      
 Min.   :0.000   Min.   :2.000   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000  
 1st Qu.:0.000   1st Qu.:2.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
 Median :1.000   Median :3.000   Median :1.000   Median :1.000   Median :1.000   Median :1.000  
 Mean   :0.524   Mean   :2.814   Mean   :1.094   Mean   :1.075   Mean   :1.069   Mean   :1.062  
 3rd Qu.:1.000   3rd Qu.:3.000   3rd Qu.:1.000   3rd Qu.:1.000   3rd Qu.:1.000   3rd Qu.:1.000  
 Max.   :1.000   Max.   :4.000   Max.   :2.000   Max.   :2.000   Max.   :2.000   Max.   :2.000  
     snap5          snap6           snap7           snap8           snap9       averBinned
 Min.   :0.00   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000   0:779     
 1st Qu.:1.00   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1:818     
 Median :1.00   Median :1.000   Median :1.000   Median :1.000   Median :1.000   2:800     
 Mean   :1.06   Mean   :1.058   Mean   :1.025   Mean   :1.174   Mean   :1.084             
 3rd Qu.:1.00   3rd Qu.:1.000   3rd Qu.:1.000   3rd Qu.:1.000   3rd Qu.:1.000             
 Max.   :2.00   Max.   :2.000   Max.   :2.000   Max.   :2.000   Max.   :2.000             

headTail(as.data.frame(E))

headTail(as.data.frame(D3))

# Save the dataset E with numerical SNAP predictors and trinary outcome to an .csv file 
# for further analysis
write.csv(E, file = "../data/inattention_nomiss_2397x12_snap_is_0_1_2_outcome_is_0_1_2.csv",row.names=FALSE)

Converting numerical variables to factors

library(xtable)

Attaching package: ‘xtable’

The following object is masked from ‘package:CORElearn’:

    display

C <- as.data.frame(C)
# select columns
cols <- c("gender", "grade", "snap1", "snap2", "snap3", "snap4", "snap5", "snap6", "snap7", "snap8", "snap9", "averBinned")
C[,cols] <- data.frame(apply(C[cols], 2, as.factor))
levels(C$gender) <- c("G", "B")
levels(C$grade) <- c("2nd", "3rd", "4th")
# N - not true (0)
# S - somewhat true (1)
# C - certainly true (2)
levels(C$snap1) <- c("N", "S", "C")  
levels(C$snap2) <- c("N", "S", "C")  
levels(C$snap3) <- c("N", "S", "C")  
levels(C$snap4) <- c("N", "S", "C")  
levels(C$snap5) <- c("N", "S", "C")  
levels(C$snap6) <- c("N", "S", "C")  
levels(C$snap7) <- c("N", "S", "C")  
levels(C$snap8) <- c("N", "S", "C")  
levels(C$snap9) <- c("N", "S", "C")
levels(C$averBinned) <- c("H", "L", "M")    # numerical order = alphabetical order
str(C)

'data.frame':   2397 obs. of  12 variables:
 $ gender    : Factor w/ 2 levels "G","B": 2 2 2 2 2 2 2 2 2 2 ...
 $ grade     : Factor w/ 3 levels "2nd","3rd","4th": 1 1 1 1 1 1 1 1 1 1 ...
 $ snap1     : Factor w/ 3 levels "N","S","C": 2 2 2 2 2 2 2 2 2 2 ...
 $ snap2     : Factor w/ 3 levels "N","S","C": 2 2 2 2 2 2 2 2 2 2 ...
 $ snap3     : Factor w/ 3 levels "N","S","C": 2 3 2 2 2 2 2 2 2 2 ...
 $ snap4     : Factor w/ 3 levels "N","S","C": 2 2 2 2 2 2 2 2 2 2 ...
 $ snap5     : Factor w/ 3 levels "N","S","C": 2 2 2 2 2 2 2 2 2 2 ...
 $ snap6     : Factor w/ 3 levels "N","S","C": 2 2 2 2 2 2 2 2 2 2 ...
 $ snap7     : Factor w/ 3 levels "N","S","C": 2 2 2 2 2 2 2 2 2 2 ...
 $ snap8     : Factor w/ 3 levels "N","S","C": 2 2 2 2 2 2 2 2 3 2 ...
 $ snap9     : Factor w/ 3 levels "N","S","C": 2 2 2 2 2 2 2 2 2 2 ...
 $ averBinned: Factor w/ 3 levels "H","L","M": 1 2 3 3 3 3 2 3 1 3 ...

headTail(C)

summary(C)

 gender   grade      snap1    snap2    snap3    snap4    snap5    snap6    snap7    snap8    snap9   
 G:1141   2nd:1008   N:  46   N:  50   N:  15   N:  16   N:  31   N:  32   N:  12   N:  93   N:  27  
 B:1256   3rd: 827   S:2079   S:2117   S:2201   S:2217   S:2190   S:2195   S:2312   S:1794   S:2142  
          4th: 562   C: 272   C: 230   C: 181   C: 164   C: 176   C: 170   C:  73   C: 510   C: 228  
 averBinned
 H:800     
 L:779     
 M:818     

xtable(summary(C))

% latex table generated in R 3.3.2 by xtable 1.8-2 package
% Sat Dec 10 16:33:42 2016
\begin{table}[ht]
\centering
\begin{tabular}{rllllllllllll}
  \hline
 & gender & grade & snap1 & snap2 & snap3 & snap4 & snap5 & snap6 & snap7 & snap8 & snap9 & averBinned \\ 
  \hline
1 & G:1141   & 2nd:1008   & N:  46   & N:  50   & N:  15   & N:  16   & N:  31   & N:  32   & N:  12   & N:  93   & N:  27   & H:800   \\ 
  2 & B:1256   & 3rd: 827   & S:2079   & S:2117   & S:2201   & S:2217   & S:2190   & S:2195   & S:2312   & S:1794   & S:2142   & L:779   \\ 
  3 &  & 4th: 562   & C: 272   & C: 230   & C: 181   & C: 164   & C: 176   & C: 170   & C:  73   & C: 510   & C: 228   & M:818   \\ 
   \hline
\end{tabular}
\end{table}

# Save the dataset C with SNAP predictors as factors and trinary outcome to an .csv file 
# for further analysis
write.csv(C, file = "../data/inattention_nomiss_2397x12_snap_is_N_S_C_outcome_is_L_M_H.csv",row.names=FALSE)

library(Hmisc)

Loading required package: survival

Attaching package: ‘survival’

The following object is masked from ‘package:boot’:

    aml

Loading required package: Formula

Attaching package: ‘Hmisc’

The following objects are masked from ‘package:xtable’:

    label, label<-

The following objects are masked from ‘package:plyr’:

    is.discrete, summarize

The following objects are masked from ‘package:memisc’:

    %nin%, html

The following object is masked from ‘package:psych’:

    describe

The following object is masked from ‘package:mlr’:

    impute

The following object is masked from ‘package:BBmisc’:

    %nin%

The following object is masked from ‘package:randomForest’:

    combine

The following objects are masked from ‘package:base’:

    format.pval, round.POSIXt, trunc.POSIXt, units

describe(C)

C 

 12  Variables      2397  Observations
-----------------------------------------------------------------------------------------------------------
gender 
       n  missing distinct 
    2397        0        2 
                      
Value          G     B
Frequency   1141  1256
Proportion 0.476 0.524
-----------------------------------------------------------------------------------------------------------
grade 
       n  missing distinct 
    2397        0        3 
                            
Value        2nd   3rd   4th
Frequency   1008   827   562
Proportion 0.421 0.345 0.234
-----------------------------------------------------------------------------------------------------------
snap1 
       n  missing distinct 
    2397        0        3 
                            
Value          N     S     C
Frequency     46  2079   272
Proportion 0.019 0.867 0.113
-----------------------------------------------------------------------------------------------------------
snap2 
       n  missing distinct 
    2397        0        3 
                            
Value          N     S     C
Frequency     50  2117   230
Proportion 0.021 0.883 0.096
-----------------------------------------------------------------------------------------------------------
snap3 
       n  missing distinct 
    2397        0        3 
                            
Value          N     S     C
Frequency     15  2201   181
Proportion 0.006 0.918 0.076
-----------------------------------------------------------------------------------------------------------
snap4 
       n  missing distinct 
    2397        0        3 
                            
Value          N     S     C
Frequency     16  2217   164
Proportion 0.007 0.925 0.068
-----------------------------------------------------------------------------------------------------------
snap5 
       n  missing distinct 
    2397        0        3 
                            
Value          N     S     C
Frequency     31  2190   176
Proportion 0.013 0.914 0.073
-----------------------------------------------------------------------------------------------------------
snap6 
       n  missing distinct 
    2397        0        3 
                            
Value          N     S     C
Frequency     32  2195   170
Proportion 0.013 0.916 0.071
-----------------------------------------------------------------------------------------------------------
snap7 
       n  missing distinct 
    2397        0        3 
                            
Value          N     S     C
Frequency     12  2312    73
Proportion 0.005 0.965 0.030
-----------------------------------------------------------------------------------------------------------
snap8 
       n  missing distinct 
    2397        0        3 
                            
Value          N     S     C
Frequency     93  1794   510
Proportion 0.039 0.748 0.213
-----------------------------------------------------------------------------------------------------------
snap9 
       n  missing distinct 
    2397        0        3 
                            
Value          N     S     C
Frequency     27  2142   228
Proportion 0.011 0.894 0.095
-----------------------------------------------------------------------------------------------------------
averBinned 
       n  missing distinct 
    2397        0        3 
                            
Value          H     L     M
Frequency    800   779   818
Proportion 0.334 0.325 0.341
-----------------------------------------------------------------------------------------------------------

library(pander)
panderOptions("digits", 5)
pander(summary(C))

-------------------------------------------------------------------------
 gender   grade    snap1   snap2   snap3   snap4   snap5   snap6   snap7 
-------- -------- ------- ------- ------- ------- ------- ------- -------
 G:1141  2nd:1008  N: 46   N: 50   N: 15   N: 16   N: 31   N: 32   N: 12 

 B:1256  3rd: 827 S:2079  S:2117  S:2201  S:2217  S:2190  S:2195  S:2312 

   NA    4th: 562 C: 272  C: 230  C: 181  C: 164  C: 176  C: 170   C: 73 
-------------------------------------------------------------------------

Table: Table continues below

 
----------------------------
 snap8   snap9   averBinned 
------- ------- ------------
 N: 93   N: 27     H:800    

S:1794  S:2142     L:779    

C: 510  C: 228     M:818    
----------------------------

pander(summary(E))

---------------------------------------------------------------------
   gender         grade         snap1         snap2         snap3    
------------- ------------- ------------- ------------- -------------
Min.  :0.000  Min.  :2.000  Min.  :0.000  Min.  :0.000  Min.  :0.000 

1st Qu.:0.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000

Median :1.000 Median :3.000 Median :1.000 Median :1.000 Median :1.000

 Mean :0.524   Mean :2.814   Mean :1.094   Mean :1.075   Mean :1.069 

3rd Qu.:1.000 3rd Qu.:3.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000

Max.  :1.000  Max.  :4.000  Max.  :2.000  Max.  :2.000  Max.  :2.000 
---------------------------------------------------------------------

Table: Table continues below

 
--------------------------------------------------------------------
    snap4        snap5         snap6         snap7         snap8    
------------- ------------ ------------- ------------- -------------
Min.  :0.000  Min.  :0.00  Min.  :0.000  Min.  :0.000  Min.  :0.000 

1st Qu.:1.000 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000

Median :1.000 Median :1.00 Median :1.000 Median :1.000 Median :1.000

 Mean :1.062   Mean :1.06   Mean :1.058   Mean :1.025   Mean :1.174 

3rd Qu.:1.000 3rd Qu.:1.00 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000

Max.  :2.000  Max.  :2.00  Max.  :2.000  Max.  :2.000  Max.  :2.000 
--------------------------------------------------------------------

Table: Table continues below

 
--------------------------
    snap9      averBinned 
------------- ------------
Min.  :0.000     0:779    

1st Qu.:1.000    1:818    

Median :1.000    2:800    

 Mean :1.084       NA     

3rd Qu.:1.000      NA     

Max.  :2.000       NA     
--------------------------

Describe subsets of data according to academic achievement and gender

C.girls.L <- C[ which(C$gender=='G' & C$averBinned=='L'), ]
C.girls.H <- C[ which(C$gender=='G' & C$averBinned=='H'), ]
C.boys.L <- C[ which(C$gender=='B' & C$averBinned=='L'), ]
C.boys.H <- C[ which(C$gender=='B' & C$averBinned=='H'), ]
summary(C.girls.L)

 gender  grade     snap1   snap2   snap3   snap4   snap5   snap6   snap7   snap8   snap9   averBinned
 G:447   2nd:183   N: 23   N: 33   N:  9   N:  9   N: 17   N: 16   N:  5   N: 48   N: 14   H:  0     
 B:  0   3rd:163   S:328   S:313   S:361   S:365   S:353   S:351   S:406   S:226   S:355   L:447     
         4th:101   C: 96   C:101   C: 77   C: 73   C: 77   C: 80   C: 36   C:173   C: 78   M:  0     

summary(C.girls.H)

 gender  grade     snap1   snap2   snap3   snap4   snap5   snap6   snap7   snap8   snap9   averBinned
 G:305   2nd:118   N:  1   N:  3   N:  0   N:  0   N:  5   N:  2   N:  0   N:  7   N:  2   H:305     
 B:  0   3rd:105   S:282   S:286   S:284   S:289   S:284   S:289   S:300   S:245   S:281   L:  0     
         4th: 82   C: 22   C: 16   C: 21   C: 16   C: 16   C: 14   C:  5   C: 53   C: 22   M:  0     

summary(C.boys.L)

 gender  grade     snap1   snap2   snap3   snap4   snap5   snap6   snap7   snap8   snap9   averBinned
 G:  0   2nd:128   N:  6   N:  3   N:  1   N:  0   N:  3   N:  3   N:  3   N: 12   N:  2   H:  0     
 B:332   3rd:100   S:284   S:284   S:311   S:304   S:303   S:299   S:321   S:245   S:295   L:332     
         4th:104   C: 42   C: 45   C: 20   C: 28   C: 26   C: 30   C:  8   C: 75   C: 35   M:  0     

summary(C.boys.H)

 gender  grade     snap1   snap2   snap3   snap4   snap5   snap6   snap7   snap8   snap9   averBinned
 G:  0   2nd:232   N:  2   N:  0   N:  0   N:  0   N:  0   N:  0   N:  0   N:  1   N:  1   H:495     
 B:495   3rd:146   S:474   S:488   S:486   S:491   S:489   S:493   S:493   S:450   S:476   L:  0     
         4th:117   C: 19   C:  7   C:  9   C:  4   C:  6   C:  2   C:  2   C: 44   C: 18   M:  0     

library(Hmisc)
describe(C.girls.L)

C.girls.L 

 12  Variables      447  Observations
-----------------------------------------------------------------------------------------------------------
gender 
       n  missing distinct    value 
     447        0        1        G 
              
Value        G
Frequency  447
Proportion   1
-----------------------------------------------------------------------------------------------------------
grade 
       n  missing distinct 
     447        0        3 
                            
Value        2nd   3rd   4th
Frequency    183   163   101
Proportion 0.409 0.365 0.226
-----------------------------------------------------------------------------------------------------------
snap1 
       n  missing distinct 
     447        0        3 
                            
Value          N     S     C
Frequency     23   328    96
Proportion 0.051 0.734 0.215
-----------------------------------------------------------------------------------------------------------
snap2 
       n  missing distinct 
     447        0        3 
                            
Value          N     S     C
Frequency     33   313   101
Proportion 0.074 0.700 0.226
-----------------------------------------------------------------------------------------------------------
snap3 
       n  missing distinct 
     447        0        3 
                            
Value          N     S     C
Frequency      9   361    77
Proportion 0.020 0.808 0.172
-----------------------------------------------------------------------------------------------------------
snap4 
       n  missing distinct 
     447        0        3 
                            
Value          N     S     C
Frequency      9   365    73
Proportion 0.020 0.817 0.163
-----------------------------------------------------------------------------------------------------------
snap5 
       n  missing distinct 
     447        0        3 
                            
Value          N     S     C
Frequency     17   353    77
Proportion 0.038 0.790 0.172
-----------------------------------------------------------------------------------------------------------
snap6 
       n  missing distinct 
     447        0        3 
                            
Value          N     S     C
Frequency     16   351    80
Proportion 0.036 0.785 0.179
-----------------------------------------------------------------------------------------------------------
snap7 
       n  missing distinct 
     447        0        3 
                            
Value          N     S     C
Frequency      5   406    36
Proportion 0.011 0.908 0.081
-----------------------------------------------------------------------------------------------------------
snap8 
       n  missing distinct 
     447        0        3 
                            
Value          N     S     C
Frequency     48   226   173
Proportion 0.107 0.506 0.387
-----------------------------------------------------------------------------------------------------------
snap9 
       n  missing distinct 
     447        0        3 
                            
Value          N     S     C
Frequency     14   355    78
Proportion 0.031 0.794 0.174
-----------------------------------------------------------------------------------------------------------
averBinned 
       n  missing distinct    value 
     447        0        1        L 
              
Value        L
Frequency  447
Proportion   1
-----------------------------------------------------------------------------------------------------------

describe(C.girls.H)

C.girls.H 

 12  Variables      305  Observations
-----------------------------------------------------------------------------------------------------------
gender 
       n  missing distinct    value 
     305        0        1        G 
              
Value        G
Frequency  305
Proportion   1
-----------------------------------------------------------------------------------------------------------
grade 
       n  missing distinct 
     305        0        3 
                            
Value        2nd   3rd   4th
Frequency    118   105    82
Proportion 0.387 0.344 0.269
-----------------------------------------------------------------------------------------------------------
snap1 
       n  missing distinct 
     305        0        3 
                            
Value          N     S     C
Frequency      1   282    22
Proportion 0.003 0.925 0.072
-----------------------------------------------------------------------------------------------------------
snap2 
       n  missing distinct 
     305        0        3 
                            
Value          N     S     C
Frequency      3   286    16
Proportion 0.010 0.938 0.052
-----------------------------------------------------------------------------------------------------------
snap3 
       n  missing distinct 
     305        0        2 
                      
Value          S     C
Frequency    284    21
Proportion 0.931 0.069
-----------------------------------------------------------------------------------------------------------
snap4 
       n  missing distinct 
     305        0        2 
                      
Value          S     C
Frequency    289    16
Proportion 0.948 0.052
-----------------------------------------------------------------------------------------------------------
snap5 
       n  missing distinct 
     305        0        3 
                            
Value          N     S     C
Frequency      5   284    16
Proportion 0.016 0.931 0.052
-----------------------------------------------------------------------------------------------------------
snap6 
       n  missing distinct 
     305        0        3 
                            
Value          N     S     C
Frequency      2   289    14
Proportion 0.007 0.948 0.046
-----------------------------------------------------------------------------------------------------------
snap7 
       n  missing distinct 
     305        0        2 
                      
Value          S     C
Frequency    300     5
Proportion 0.984 0.016
-----------------------------------------------------------------------------------------------------------
snap8 
       n  missing distinct 
     305        0        3 
                            
Value          N     S     C
Frequency      7   245    53
Proportion 0.023 0.803 0.174
-----------------------------------------------------------------------------------------------------------
snap9 
       n  missing distinct 
     305        0        3 
                            
Value          N     S     C
Frequency      2   281    22
Proportion 0.007 0.921 0.072
-----------------------------------------------------------------------------------------------------------
averBinned 
       n  missing distinct    value 
     305        0        1        H 
              
Value        H
Frequency  305
Proportion   1
-----------------------------------------------------------------------------------------------------------

describe(C.boys.L)

C.boys.L 

 12  Variables      332  Observations
-----------------------------------------------------------------------------------------------------------
gender 
       n  missing distinct    value 
     332        0        1        B 
              
Value        B
Frequency  332
Proportion   1
-----------------------------------------------------------------------------------------------------------
grade 
       n  missing distinct 
     332        0        3 
                            
Value        2nd   3rd   4th
Frequency    128   100   104
Proportion 0.386 0.301 0.313
-----------------------------------------------------------------------------------------------------------
snap1 
       n  missing distinct 
     332        0        3 
                            
Value          N     S     C
Frequency      6   284    42
Proportion 0.018 0.855 0.127
-----------------------------------------------------------------------------------------------------------
snap2 
       n  missing distinct 
     332        0        3 
                            
Value          N     S     C
Frequency      3   284    45
Proportion 0.009 0.855 0.136
-----------------------------------------------------------------------------------------------------------
snap3 
       n  missing distinct 
     332        0        3 
                            
Value          N     S     C
Frequency      1   311    20
Proportion 0.003 0.937 0.060
-----------------------------------------------------------------------------------------------------------
snap4 
       n  missing distinct 
     332        0        2 
                      
Value          S     C
Frequency    304    28
Proportion 0.916 0.084
-----------------------------------------------------------------------------------------------------------
snap5 
       n  missing distinct 
     332        0        3 
                            
Value          N     S     C
Frequency      3   303    26
Proportion 0.009 0.913 0.078
-----------------------------------------------------------------------------------------------------------
snap6 
       n  missing distinct 
     332        0        3 
                            
Value          N     S     C
Frequency      3   299    30
Proportion 0.009 0.901 0.090
-----------------------------------------------------------------------------------------------------------
snap7 
       n  missing distinct 
     332        0        3 
                            
Value          N     S     C
Frequency      3   321     8
Proportion 0.009 0.967 0.024
-----------------------------------------------------------------------------------------------------------
snap8 
       n  missing distinct 
     332        0        3 
                            
Value          N     S     C
Frequency     12   245    75
Proportion 0.036 0.738 0.226
-----------------------------------------------------------------------------------------------------------
snap9 
       n  missing distinct 
     332        0        3 
                            
Value          N     S     C
Frequency      2   295    35
Proportion 0.006 0.889 0.105
-----------------------------------------------------------------------------------------------------------
averBinned 
       n  missing distinct    value 
     332        0        1        L 
              
Value        L
Frequency  332
Proportion   1
-----------------------------------------------------------------------------------------------------------

describe(C.boys.H)

C.boys.H 

 12  Variables      495  Observations
-----------------------------------------------------------------------------------------------------------
gender 
       n  missing distinct    value 
     495        0        1        B 
              
Value        B
Frequency  495
Proportion   1
-----------------------------------------------------------------------------------------------------------
grade 
       n  missing distinct 
     495        0        3 
                            
Value        2nd   3rd   4th
Frequency    232   146   117
Proportion 0.469 0.295 0.236
-----------------------------------------------------------------------------------------------------------
snap1 
       n  missing distinct 
     495        0        3 
                            
Value          N     S     C
Frequency      2   474    19
Proportion 0.004 0.958 0.038
-----------------------------------------------------------------------------------------------------------
snap2 
       n  missing distinct 
     495        0        2 
                      
Value          S     C
Frequency    488     7
Proportion 0.986 0.014
-----------------------------------------------------------------------------------------------------------
snap3 
       n  missing distinct 
     495        0        2 
                      
Value          S     C
Frequency    486     9
Proportion 0.982 0.018
-----------------------------------------------------------------------------------------------------------
snap4 
       n  missing distinct 
     495        0        2 
                      
Value          S     C
Frequency    491     4
Proportion 0.992 0.008
-----------------------------------------------------------------------------------------------------------
snap5 
       n  missing distinct 
     495        0        2 
                      
Value          S     C
Frequency    489     6
Proportion 0.988 0.012
-----------------------------------------------------------------------------------------------------------
snap6 
       n  missing distinct 
     495        0        2 
                      
Value          S     C
Frequency    493     2
Proportion 0.996 0.004
-----------------------------------------------------------------------------------------------------------
snap7 
       n  missing distinct 
     495        0        2 
                      
Value          S     C
Frequency    493     2
Proportion 0.996 0.004
-----------------------------------------------------------------------------------------------------------
snap8 
       n  missing distinct 
     495        0        3 
                            
Value          N     S     C
Frequency      1   450    44
Proportion 0.002 0.909 0.089
-----------------------------------------------------------------------------------------------------------
snap9 
       n  missing distinct 
     495        0        3 
                            
Value          N     S     C
Frequency      1   476    18
Proportion 0.002 0.962 0.036
-----------------------------------------------------------------------------------------------------------
averBinned 
       n  missing distinct    value 
     495        0        1        H 
              
Value        H
Frequency  495
Proportion   1
-----------------------------------------------------------------------------------------------------------